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The modern age of professional competition and the post-COVID-19 situation led to the birth of stressful life. The lifestyle of medical practitioners becomes hectic and disturbing, affecting not only their profession but also their personal lives. This study identifies the factors like Role Overload & Responsibility (ROR), Lifestyle Variations (LV), Family Disruptions (FD), and Stringent Working Conditions (SWC) affecting the occupational performance of doctors. This was exploratory research that identified and validated antecedent work stress factors for the occupational performance of medical professionals in the national capital region. The factors were measured through a self-adapted questionnaire based on the five-point Likert scale. The validation of the factors was verified through the hypothesized model by using structural equation modeling (SEM) on their occupational performance. The study identifies that ROR and SWC affect the National Capital Region (NCR) medical practitioners' occupational performance. Moreover, this study has specific implications for medical professionals and provides the theoretical and practical basis for health issues during the job that severely impacts the employees' personal and professional lives. This study decodes the work stress buster factors that manipulated the effective occupational performance of the employees.

The advances in digital health, including mobile healthcare (mHealth) medication adherence applications (MApps), have been demonstrated to support medication adherence and improve health outcomes. This study aims to evaluate the knowledge and perception of the MApps among pharmacists and pharmacy students. An online cross-sectional survey was conducted among 223 pharmacists and pharmacy students in the Jazan region of Saudi Arabia between 1st and 30th April 2023. The survey collected information about the participants’ socio-demographics, knowledge, and perception of the MApps. Among the 223 participants included in the study, 105 (47.1%) were pharmacists and 118 (52.9%) were pharmacy students. Most participants were females (72.6%) and aged 18–30 (70.4%). About half of the participants had poor knowledge of the MApps [pharmacists (48.0%) and students (42.0%)] and mainly encountered Medisafe (18.1%) or Pills (17.0%) MApps, respectively. Pharmacy students showed significantly higher knowledge of MApps ( p = 0.048), especially the Pills ( p = 0.022) than pharmacists. However, the pharmacists had significantly higher knowledge of MyMeds ( p = 0.001) than pharmacy students. Most participants had a positive perception of the usefulness of the MApps (pharmacists, 79.0%; students 80.0%). Notably, over 85% of the participants expressed willingness to know and provide guidance on MApps, with over 50% willing to recommend it to the patients. There was no significant difference in perception between the pharmacists and pharmacy students ( p >0.05). In conclusion, the study demonstrates limited knowledge with a positive perception of mHealth medication adherence applications among pharmacists and pharmacy students. Integrating digital adherence tools like the MApps into pharmacy training could significantly improve professional practice mHealth competencies, and optimize healthcare delivery and patient outcomes.

This study aimed to explore knowledge, attitude, perceptions, and willingness regarding vaccination among university students in Pakistan. This cross-sectional study was carried out using an open online self-administered survey via Google Forms. The survey data were collected between the 15 to 30 of October 2022. A total of 946 respondents participated in the study, of which the majority were female (514, 54.3%). Most students belonged to a medical background, specifically pharmaceutical sciences. Most of the respondents did not know about monkeypox before 2022 (646, 68.3%). Regarding overall knowledge of monkeypox, most of the respondents had average knowledge (726, 76.7%), with very few having good knowledge (60, 6.3%). Regarding overall attitudes towards monkeypox, most of the respondents had neutral attitudes (648, 68.5%). There was a significant association between knowledge of Monkeypox with the type of academic degree (p < 0.001), type of discipline (p < 0.001), and region of respondents (p < 0.001). The willingness to vaccinate among the population was (67.7%). The current study pointed out that the overall knowledge of monkeypox was average in most respondents, with considerable knowledge gaps in most aspects. The overall attitude towards monkeypox was neutral. Further, the knowledge about monkeypox was strongly associated with academic degree, study discipline, and region of respondents. Our findings emphasize the need to raise public awareness by educating students on the monkeypox virus. This will improve adherence to preventative recommendations.

Background and objective: Adverse drug reactions (ADRs) cause substantial harm, and a considerable proportion may be preventable, but under-reporting persists and weakens pharmacovigilance. Spontaneous reporting depends on clinicians, yet under-reporting persists and weakens pharmacovigilance. To map barriers to adverse drug reaction reporting among physicians, a comparison of nurses and pharmacists in single-country studies was carried out between high-income countries (HICs) and low- and middle-income countries (LMICs). Methods: A scoping review was conducted following PRISMA-ScR guidance. PubMed and Web of Science were searched for studies published from 2016 onward. Eligible studies were single-country primary empirical studies including physicians, nurses, or pharmacists and examining ADR reporting. Only barriers that were measured or explicitly explored and reported as extractable results were included. Barriers were coded into 12 domains and summarised by income group and profession. Results: A total of 44 studies were included, with 18 from HICs and 26 from LMICs. Survey designs were most common. Pharmacists were the most frequently studied cadre. Knowledge and training barriers were reported in all studies in both income groups. Fear of legal or punitive concerns was reported in 13/18 (72.2%) HIC studies and 17/26 (65.4%) LMIC studies. Time and workload barriers were reported in 10/18 (55.6%) HIC studies and 11/26 (42.3%) LMIC studies. Access barriers to tools, forms, and information technology showed the clearest income group difference: these were reported in 5/18 (27.8%) HIC studies versus 16/26 (61.5%) LMIC studies. Lack of feedback or acknowledgement was reported in 8/18 (44.4%) HIC studies and 10/26 (38.5%) LMIC studies. Conclusions: Barriers extend beyond individual knowledge in all settings. The main income group difference was the greater prominence of reporting system access barriers in LMICs compared with workflow and time pressure barriers in HICs. Addressing fear and building a supportive non-punitive reporting culture remains a cross-cutting priority because these were common issues in both income groups and can limit reporting even when infrastructure and training exist.

The application of machine learning (ML) methods and predictive analytics in disaster management has made a drastic change in this field over the past few years. With their unparalleled ability to forecast, prepare, and respond, these advanced technologies are transforming the complete paradigm of disaster and emergency management. Much of this work is reinforced by machine learning models, an artificial intelligence domain that analyses huge amounts of data to establish patterns and forecast future disasters. This research proposes novel techniques in disaster management-based healthcare system utilizing machine learning model for sustainable environment. The study utilizes a dataset Centre for Research on Epidemiology of Disasters (CRED) launched Emergency Events Database (EM-DAT) in 1988. Data on frequency as well as effects of about 15,700 incidents since 1900 can be found in International Disaster Database, or EM-DA, which is preprocessed for noise removal and normalization. The processed data features have been extracted utilizing deep adversarial gaussian multilayer perceptron and the features has been optimized using firefly swarm binary grasshopper optimization. Experimental analysis is carried out in terms of random accuracy, precision, recall, AUC, F-1 score. Proposed technique random accuracy 98%, precision 95%, F-1 score 94%, AUC 96%, Recall 97%.

COVID-19 patients with already existing chronic medical conditions are more likely to develop severe complications and, ultimately, a higher risk of mortality. This study analyzes the impacts of pre-existing chronic illnesses such as diabetes (DM), hypertension, and cardiovascular diseases (CVDs) on COVID-19 cases by using radiological chest imaging. The data of laboratory-confirmed COVID-19-infected hospitalized patients were analyzed from March 2020 to December 2020. Chest X-ray images were included to further identify the differences in X-ray patterns of patients with co-morbid conditions and without any co-morbidity. The Pearson chi-square test checks the significance of the association between co-morbidities and mortality. The magnitude and dimension of the association were calibrated by the odds ratio (OR) at a 95% confidence interval (95% CI) over the patients' status (mortality and discharged cases). A univariate binary logistic regression model was applied to examine the impact of co-morbidities on death cases independently. A multivariate binary logistic regression model was applied for the adjusted effects of possible confounders. For the sensitivity analysis of the model, receiver operating characteristic (ROC) was applied. Patients with different comorbidities, including diabetes (OR = 33.4, 95% CI: 20.31-54.78, p < 0.001), cardiovascular conditions (OR = 24.14, 95% CI: 10.18-57.73, p < 0.001), and hypertension (OR = 16.9, 95% CI: 10.20-27.33, p < 0.001), showed strong and significant associations. The opacities present in various zones of the lungs clearly show that COVID-19 patients with chronic illnesses such as diabetes, hypertension, cardiovascular disease, and obesity experience significantly worse outcomes, as evidenced by chest X-rays showing increased pneumonia and deterioration. Therefore, stringent precautions and a global public health campaign are crucial to reducing mortality in these high-risk groups.

Up-to-date estimates of stroke burden and attributable risks and their trends at global, regional, and national levels are essential for evidence-based health care, prevention, and resource allocation planning. We aimed to provide such estimates for the period 1990–2021. We estimated incidence, prevalence, death, and disability-adjusted life-year (DALY) counts and age-standardised rates per 100 000 people per year for overall stroke, ischaemic stroke, intracerebral haemorrhage, and subarachnoid haemorrhage, for 204 countries and territories from 1990 to 2021. We also calculated burden of stroke attributable to 23 risk factors and six risk clusters (air pollution, tobacco smoking, behavioural, dietary, environmental, and metabolic risks) at the global and regional levels (21 GBD regions and Socio-demographic Index [SDI] quintiles), using the standard GBD methodology. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. In 2021, stroke was the third most common GBD level 3 cause of death (7·3 million [95% UI 6·6–7·8] deaths; 10·7% [9·8–11·3] of all deaths) after ischaemic heart disease and COVID-19, and the fourth most common cause of DALYs (160·5 million [147·8–171·6] DALYs; 5·6% [5·0–6·1] of all DALYs). In 2021, there were 93·8 million (89·0–99·3) prevalent and 11·9 million (10·7–13·2) incident strokes. We found disparities in stroke burden and risk factors by GBD region, country or territory, and SDI, as well as a stagnation in the reduction of incidence from 2015 onwards, and even some increases in the stroke incidence, death, prevalence, and DALY rates in southeast Asia, east Asia, and Oceania, countries with lower SDI, and people younger than 70 years. Globally, ischaemic stroke constituted 65·3% (62·4–67·7), intracerebral haemorrhage constituted 28·8% (28·3–28·8), and subarachnoid haemorrhage constituted 5·8% (5·7–6·0) of incident strokes. There were substantial increases in DALYs attributable to high BMI (88·2% [53·4–117·7]), high ambient temperature (72·4% [51·1 to 179·5]), high fasting plasma glucose (32·1% [26·7–38·1]), diet high in sugar-sweetened beverages (23·4% [12·7–35·7]), low physical activity (11·3% [1·8–34·9]), high systolic blood pressure (6·7% [2·5–11·6]), lead exposure (6·5% [4·5–11·2]), and diet low in omega-6 polyunsaturated fatty acids (5·3% [0·5–10·5]). Stroke burden has increased from 1990 to 2021, and the contribution of several risk factors has also increased. Effective, accessible, and affordable measures to improve stroke surveillance, prevention (with the emphasis on blood pressure, lifestyle, and environmental factors), acute care, and rehabilitation need to be urgently implemented across all countries to reduce stroke burden. Bill & Melinda Gates Foundation.

Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6–47·0) in 1990 to 63·4 years (63·1–63·7) in 2023. For males, mean age increased from 45·4 years (45·1–45·7) to 61·2 years (60·7–61·6), and for females it increased from 48·5 years (48·1–48·8) to 65·9 years (65·5–66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9–81·0) and for males 74·8 years (74·8–74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5–38·4) for females and 35·6 years (35·2–35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. We examined global mortality patterns over the past three decades, highlighting—with enhanced estimation methods—the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Gates Foundation.

[This corrects the article DOI: 10.1371/journal.pone.0308187.].

Snakebite envenoming remains a significant yet neglected public health problem in tropical countries, particularly in rural South Asia. This study aimed to identify demographic characteristics, management practices, and the determinants of poor treatment outcomes among snakebite patients in Sindh, Pakistan. A prospective cohort study was conducted at Peoples Medical College Hospital (PMCH), Shaheed Benazirabad, Sindh, Pakistan, from July 1, 2023, to June 30, 2024. A non-probability purposive sampling technique was used for data collection, and all consecutive patients presenting with confirmed or suspected snakebite were included. Data were collected through a validated study tool on demographics, pre-hospital management, hospital care, and treatment outcomes. Categorical variables were tested with the Chi-square test, and Kaplan-Meier survival analysis was used to test the effect of exposure-to-reporting time and hospital stay time on outcomes using IBM SPSS V29. A total of 320 patients were included; 74.7% were male, and 98.4% were from rural areas. Most victims were aged 20-29 years (31.9%) and engaged in farming or manual labor (67.2%). Nearly half (49.7%) of the bites occurred during summer. Delayed hospital presentation was common, with 22.8% arriving after six hours of the bite. The overall poor-outcome rate was 10.9%, and mortality was 1.9%. A significant association was found between exposure-to-reporting time ( = 0.040) and hospital stay duration ( < 0.001) with treatment outcomes. Delayed presentation to the hospital and prolonged hospitalization were major predictors of poor outcomes following snakebite. Strengthening emergency referral systems, ensuring timely antivenom availability, and promoting community awareness are essential to reduce morbidity and mortality in snakebite-endemic regions of Pakistan.